# Robot Planning with Mathematical Models of Human State and Action

arXiv: https://arxiv.org/abs/1705.04226 · HTML: https://mlsanity.com/paper/1705.04226/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 7 of 7)

- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)
- [When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor](https://mlsanity.com/paper/2602.18850/) (2026)
- [One-shot Policy Elicitation via Semantic Reward Manipulation](https://mlsanity.com/paper/2101.01860/) (2021)
- [Balancing Performance and Human Autonomy with Implicit Guidance Agent](https://mlsanity.com/paper/2109.00414/) (2021)
- [Explainable Human-AI Interaction: A Planning Perspective](https://mlsanity.com/paper/2405.15804/) (2024)
- [HumanFlow -- Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control](https://mlsanity.com/paper/2605.25685/) (2026)

## Abstract (arXiv)

Robots interacting with the physical world plan with models of physics. We advocate that robots interacting with people need to plan with models of cognition. This writeup summarizes the insights we have gained in integrating computational cognitive models of people into robotics planning and control. It starts from a general game-theoretic formulation of interaction, and analyzes how different approximations result in different useful coordination behaviors for the robot during its interaction with people.